2019-20 Catalog

Statistics (STAT)

Courses

STAT 342 Applied Linear Algebra 3 Credits

The theoretical basis for applying linear algebra in other fields, including statistics. Topics will include systems of equations, vector spaces, matrices, and linear transformations. Additional topics will include matrix factorizations (including LU, QR, eigen-decomposition, and SVD) and how they can be used in computer analysis of data sets. Some students may optionally choose to take MATH 205 as preparation for this course. Not available for credit to students who have completed MATH 241 or MATH 242.
Prerequisites: MATH 022

STAT 408 Seminar in Statistics and Probability 1-6 Credits

Intensive study of some field of statistics or probability not offered in another course. Consent of department required.

STAT 409 Seminar in Statistics and Probability 1-6 Credits

Intensive study of some field of statistics or probability not offered in another course. Consent of department required.

STAT 410 Random Processes and Applications 3 Credits

See MATH 310.

STAT 412 Advanced Applied Statistics 3 Credits

Selected advanced topics in applied statistics. Possible topics include nonparametric statistics, multivariate statistics, generalized linear model, survival analysis, time series analysis or other modern applied statistical methods with application to real world problems. Topics could vary from one semester to another depending on the interests of the faculty member and the students.
Repeat Status: Course may be repeated.

STAT 434 Mathematical Statistics 3 Credits

See MATH 334.

STAT 438 Linear Models In Statistics with Applications 3 Credits

See MATH 338.

STAT 461 Topics In Mathematical Statistics 3 Credits

See MATH 461.

STAT 462 Modern Nonparametric Methods in Statistics 3 Credits

See MATH 462.

STAT 463 (MATH 463) Advanced Probability 3 Credits

See MATH 463.
Prerequisites: MATH 309 and MATH 401

STAT 464 Advanced Stochastic Processes 3 Credits

See MATH 464.

STAT 471 Topics in Statistical Learning and Computing 3 Credits

Selected advanced topics in statistical learning and computing. Possible topics include linear and nonlinear regression, applied spatial statistics, applied multivariate and longitudinal data analysis, functional data analysis, survival analysis, data analytics, statistical methods that use intensive-computing or simulations, data mining techniques, with application and interpretation of a variety of statistical methods in real world problems. Topics could vary from one semester to another depending on the interests of the faculty member and the students.
Repeat Status: Course may be repeated.